Position determination system having a deconvolution decoder using a joint SNR-time of arrival approach

ABSTRACT

The present disclosure relates to an acoustic position determination system that includes a mobile communication device and at least one base transmitter unit. The mobile communication device is configured to identify a peak in the received signal, and to de-convolve the signal with all codes that are relevant to the area in which the signal is received. A joint likelihood that a potential code is correct is formed by determining a likelihood based on a signal parameter such as signal-to-noise ratio and a likelihood based on time-of arrival information.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. non-provisional patentapplication Ser. No. 17/316,426, titled “Position Determination SystemHaving a Deconvolution Decoder Using a Joint SNR-Time of ArrivalApproach,” which was filed on May 10, 2021, which is a continuation ofU.S. non-provisional patent application Ser. No. 15/858,683, titled“Position Determination System Having a Deconvolution Decoder Using aJoint SNR-Time of Arrival Approach,” which was filed on Dec. 29, 2017and issued as U.S. Pat. No. 11,002,825 on May 11, 2021, all of which areincorporated herein by reference in their entirety.

FIELD

The present disclosure relates generally to real-time locating systems,and more particularly to determining a location of a mobile device basedat least in part on acoustic-contextual data associated with a real-timelocating system.

BACKGROUND

A common challenge in modern business is to locate important resourcesat any given time in a building or campus environment. Such resourcesinclude key personnel, critical pieces of equipment, vital records andthe like. For example, the personnel, the critical pieces of equipmentand the vital records are typically mobile, are often needed in avariety of locations during a typical working day, and are thereforeconstantly being relocated during the working day. Given that it isunproductive to divert other resources to locate these resources, it isdesirable to develop an approach that can locate these importantresources at any time in the environment of a building, campusenvironment and the like.

BRIEF SUMMARY

Certain embodiments of the present invention relate to acomputer-implemented method of determining an identity of a transmittingdevice that includes receiving, by a mobile receiving unit, an acousticsignal from the transmitting device through a plurality of transmissionpaths, wherein the acoustic signal comprises one of a plurality of codekeys. The method further includes correlating the acoustic signal with amagnitude block window to determine a signal peak, the signal peakhaving a start and an end. In addition, the method includes deconvolvingthe signal peak with each of the plurality of code keys to yield a setof valid code keys from the plurality of code keys, each valid code keyexceeding a predetermined signal-noise-ratio threshold. Still further,the method includes determining a first likelihood of correctness foreach valid code key based on at least one parameter associated with theacoustic signal, as well as determining a second likelihood ofcorrectness for each valid code key based on a time of arrival. Themethod also includes identifying a code key associated with the identityof the transmitting device based on a joint likelihood of correctnessbased on the first likelihood of correctness and the second likelihoodof correctness.

In some embodiments, a mobile communication device is configured todetermine an identity of a transmitting device, where the mobilecommunication device includes a receiver configured to receive anacoustic signal from the transmitting device through a plurality oftransmission paths, wherein the acoustic signal comprises one of aplurality of code keys. The mobile communication device also includes aprocessing unit that is configured to correlate the acoustic signal witha magnitude block window to determine a signal peak, the signal peakhaving a start and an end. The processing unit is further configured todeconvolve the signal peak with each of the plurality of code keys toyield a set of valid code keys from the plurality of code keys, eachvalid code key exceeding a predetermined signal-noise-ratio threshold.In addition, the processing unit is configured to determine a firstlikelihood of correctness for each valid code key based on at least oneparameter associated with the acoustic signal, as well as determine asecond likelihood of correctness for each valid code key based on a timeof arrival. The processing unit is further configured to identify a codekey associated with the identity of the transmitting device based on ajoint likelihood of correctness based on the first likelihood ofcorrectness and the second likelihood of correctness.

Further features and advantages of the present invention, as well as thestructure and operation of various embodiments thereof, are described indetail below with reference to the accompanying drawings. It is notedthat the invention is not limited to the specific embodiments describedherein. Such embodiments are presented herein for illustrative purposesonly. Additional embodiments will be apparent to persons skilled in therelevant art(s) based on the teachings contained herein.

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

Reference will be made to the embodiments of the invention, examples ofwhich may be illustrated in the accompanying figures. These figures areintended to be illustrative, not limiting. Although the invention isgenerally described in the context of these embodiments, it should beunderstood that it is not intended to limit the scope of the inventionto these particular embodiments.

FIG. 1 is a perspective representation of a position determinationsystem, in accordance with some embodiments.

FIG. 2 is a flow chart of a method of performing signal magnitudeanalysis, in accordance with some embodiments.

FIG. 3 is a flow chart of a method of determining position informationof a mobile communication device, in accordance with some embodiments.

FIG. 4 is a schematic representation of the response function fordeconvolution with a transmitted signal code, in accordance with someembodiments.

FIG. 5 is a flow chart of a method of performing peak analysis in thereceived acoustic signals, in accordance with some embodiments.

FIG. 6 is a flow chart of a method of determining Doppler velocities forall signal transmission paths using non-linear fitting model, inaccordance with some embodiments.

DETAILED DESCRIPTION

While the present disclosure is made with reference to illustrativeembodiments for particular applications, it should be understood thatthe disclosure is not limited thereto. Those skilled in the art withaccess to the teachings herein will recognize additional modifications,applications, and embodiments within the scope thereof and additionalfields to which the disclosure would apply.

Indoor real-time location systems are used to determine the location ofa moveable object, such as a person or an item of equipment, within anindoor environment such as hospitals, offices, or warehouses. Indoorreal-time location systems can operate with different levels of accuracydepending on the system infrastructure and provide a three-dimensionalposition information regarding the person or equipment. An indoorreal-time location system can include a network of transmitter stationsattached to interior surfaces of the indoor environment and mobilecommunication devices attached to moveable objects. The mobilecommunication devices can communicate with one or more of thetransmitter stations to determine a three-dimensional positioninformation of the mobile communication device within the indoorenvironment. Acoustic is well suited to this purpose as it travelsslower than radio waves and is generally unnoticeable to humans.Acoustic waves also attenuate more rapidly and are less likely topenetrate walls so signal interferences between rooms can be minimized.Acoustic signals are also easier to process for determining the typicalrelatively short distances between transmitter stations and thereceiver.

However, such an approach has limitations. Mobile communication devicesare often attached to objects or persons that move with reference to thetransmitter stations. It is challenging to determine a real-timelocation of a mobile communication device which is moving and therebycreating a Doppler shift in the received signals. In addition, theaccuracy in determining the identity and position of the mobilecommunication device can be adversely affected by multi-pathinterference, which is commonly present due to reflections of thetransmitted signal from walls, ceilings and other surfaces.

Therefore, there remains a need for indoor real-time location systemsthat provide accurate position information for moving mobilecommunication devices in an indoor environment. The estimation of thelocation of a mobile communication device (e.g., mobile communicationdevice) according to example aspects of the present disclosure canprovide more accurate and efficient locating techniques relative toconventional real-time locating systems. In particular, the knowledge ofan acoustic environment (e.g. acoustic transmitter locations relative tothe reflective surfaces) facilitates the location estimation. Suchtechniques can provide location estimations with an accuracy of betweenabout 2.5 centimeters (1 inch) and about 25 centimeters (10 inches) ofstandard deviation.

More particularly, upon entry into an environment having a transmittingdevice, the mobile communication device can receive acoustic signalsfrom the transmitting device. Such received signals can correspond to asignal propagating directly from the transmitting device to the mobilecommunication device, as well as to one or more signals that have beenreflected by a reflective surface within the environment. The locationof the mobile communication device can be estimated based at least inpart on the received acoustic signals and an acoustic model representingthe environment.

In particular, the peaks in the received acoustic signals may be used todetermine location of the mobile communication device. For instance, afirst set of peaks (e.g. a set of two peaks) can be selected from thereceived acoustic signals. More particularly, such peaks can be selectedfrom a time domain representation of the magnitude of the receivedsignals. The peaks can be selected based at least in part on anamplitude of each peak and an order of occurrence of the peaks. Forinstance, the selected peaks can include the first two received peakshaving an amplitude greater than a threshold. In this manner, the peakscan be selected based on an assumption that the selected peaks werecaused by low order transmitter locations (e.g. the 0^(th) ordertransmitter location and a 1^(st) order transmitter location). Each peakof the set of selected peaks can be assigned to a transmitter locationbased at least in part on the acoustic model. The assignment can bedetermined based at least in part on an orientation of the mobilecommunication device with respect to the transmitter locations, and anangular sensitivity of one or more acoustic transducers (e.g.microphones) of the mobile communication device, and/or an angularsensitivity of a transducer of the acoustic transmitting device. Theorientation of the mobile communication device can be determined, forinstance, using one or more onboard sensors of the mobile communicationdevice. The orientation can be determined with respect to thetransmitter locations based at least in part on the known locations ofthe transmitter locations defined by the acoustic model.

The selected peaks can be selected to correspond to signals associatedwith a 0^(th) order transmitter location and one or more 1^(st) ordertransmitter locations based at least in part on the assumption that theorder of arrival of the peaks can correspond to the reflection ordersassociated with the transmitter locations due to the fact that theacoustic signals associated with higher reflection orders generallytravel greater distances that acoustic signals associated with lowerreflection orders. In this manner, the peaks can be selected based atleast in part on the assumption that a signal (e.g. peak) from a 0^(th)order transmitter location will arrive prior to a signal from a 1^(st)order transmitter location, which will arrive prior to a signal from a2^(nd) order transmitter location. Similarly, the assignment of thetransmitter locations to the peaks can reflect the order of arrival ofthe peaks. For instance, the first peak can be assigned to the 0^(th)order transmitter location, and the second peak can be assigned to a1^(st) order transmitter location. As indicated, in someimplementations, the peak assignments can be determined based at leastin part on the orientation of the mobile communication device withrespect to the transmitter locations, and an angular sensitivity of oneor more acoustic transducers (e.g. microphones) of the mobilecommunication device. In such implementations, the reflection ordersassociated with the transmitter locations can aid in the transmitterlocation assignments, and thereby a determination of the location of themobile communication device, as discussed further below. Although thedescription to follow operates on acoustic signals, those skilled in therelevant art(s) will recognize that the operations described here can besimilarly applied to other type of signals such as, for example,electromagnetic signals, phase shift key signals, orthogonal orsemi-orthogonal signals, quadrature amplitude modulated (QAM) signals,any other suitable signals, without departing from the spirit and scopeof the present disclosure.

FIG. 1 is a perspective representation of a position determinationsystem 100. Position determination system 100 can be an indoor real-timelocation systems used to determine the location of a moveable object.Position determination system 100 can include an indoor environment 102,a transmitter station 104, mobile communication device 106, and a remoteprocessing unit 108. These components cooperate to provide a positioningsystem, capable of estimating a three-dimensional location of the mobilecommunication device 106 within indoor environment 102. In someembodiments, position determination system 100 can have more than onetransmitter stations, installed throughout a building or series ofrooms, and more than one mobile communication devices attached to, orincorporated into, people, animals, vehicles, robots, stock, equipment,etc. Indoor environment 102 can be rooms in a building such as, forexample, a ward in a hospital, an office in an office building, or astorage space in a warehouse.

Transmitter station 104 can include an acoustic sounder and processinglogic for causing the acoustic sounder to transmit acoustic signals 110.In certain embodiments, the acoustic signals 110 are acoustic signals.The acoustic signals 110 transmitted by transmitter station 104 caninclude a signature unique to the specific transmitter station itself.As described above, position determination system 100 can include morethan one transmitter stations, and each transmitter station can beconfigured to transmit signals containing a signature unique to thatspecific transmitter station. Each signature is encoded on an acousticcarrier having an acoustic frequency such as, for example, 20 kHz, 40kHz, ultrasonic or any other suitable acoustic frequencies. Thesignature can comprise a respective one of a set of sixty-fourQPSK-encoded Complementary-Code-Keying (CCK) codes. The signature can beeasily extended in units of 2 bits (di-bit) to construe a signature ofany desirable length. The signature can be contained in a longertransmission that also has one or more additional elements, such as apreamble and/or data content, preferably also QPSK-encoded on the sameacoustic carrier. In some embodiments, each transmitter station can beassigned a unique time slot during which its signal can be transmitted,and the receiver can identify the origin of a received signal based onthe receiving time of the signal. Therefore, multiple transmitterstations can be within audible distances from each other transmittingwithin a certain time frame. In some embodiments, both the signature andtime slot methods are used to identify the transmitter stations in thereceived signal.

Mobile communication device 106 can include a microphone capable ofreceiving acoustic signals 110 from transmitter station 104 and aprocessing unit for sampling and processing the received acousticsignals. Mobile communication device 106 is situated within the indoorenvironment 102, not immediately adjacent either of the walls or theceiling. It may be attached to a person or item of equipment (notshown). In addition, mobile communication device 106 can be configuredto transmit an acoustic signal substantially evenly throughout theindoor environment. Alternatively, it may emit sound with a directionalpattern into part of the room. In some embodiments, mobile communicationdevice 106 may include any suitable devices such as, for example, a cellphone, an acoustic transducer, an acoustic tag, and/or any othersuitable devices. In some embodiments, mobile communication device 106may not carry out all the processing using its own processing unit, butmay share the processing with a remote computer such as the remoteprocessing server 108 by transmitting relevant data to the remoteprocessing server 108 using acoustic or radio. The mobile communicationdevice 106 and/or transmitter station 104 may comprise a wired orwireless transmitter, such as a radio transmitter, for transmittinginformation relating to a received or transmitted signal.

FIG. 2 is a flow chart of an exemplary method 200 of performing signalmagnitude analysis, in accordance with some embodiments. The methoddescribed herein determines signal portions (i.e., signal packets) ofthe received acoustic signals that may contain signaling content. Otheroperations in method 200 can be performed and the operations can beperformed in a different order and/or vary.

At operation 202, magnitudes of samples are calculated and analyzed. TheIn-phase and Quadrature (“IQ”) (complex sampled baseband) data resultingfrom the mix and decimation process can be oversampled by a factor of 2to 8, to obtain better time and frequency resolution which is criticalin the estimation of Doppler velocity. When no Doppler shift is present,consecutive samples within one chip should have the same modulated phaseand therefore should have approximately zero phase difference betweenthem. However, if there is an approximately-constant, non-zero phasederivative across consecutive samples, this is indicative of aphase-shift due to Doppler shift. In some embodiments, the data samplingcan be performed at the critical sampling rate (i.e., the chip samplerate equals to the chip rate). Magnitude of each IQ sample is calculatedand the average magnitude is calculated over the symbol length for allsampling positions as they become available. The average magnitude isdifferentiated using a differentiation filter with a length of typicallyhalf the symbol length. In some embodiments, Lanczos or finite impulseresponse (FIR) differentiation filter can be used.

At operation 204, valid peaks are identified. Peaks in the averagedmagnitudes are identified by searching for negative going zero crossingsof the differentiated magnitude data and requiring that the magnitudevalue is over a certain threshold value. In some embodiments, thethreshold value can be calculated dynamically based on a statisticalanalysis of the incoming IQ or magnitude data. For example, thethreshold value can be a running average of the lowest 10 percentile ofmagnitudes. In some embodiments, the data can be delayed by a knownamount of samples due to the filter length. For example, the delay canbe n number of samples for a Lanczos filter with filter length 2n+1.

At operation 206, data surrounding a valid peak is analyzed. IQ datasurrounding a valid peak can be sent to a multipath symbol sample windowextractor algorithm for further analysis. In some embodiments, thesurrounding data can be 128 samples. The multipath symbol sample windowextractor is critical in a deconvolution approach since signal portionsthat contain incomplete symbols severely compromise the deconvolutionresult. It is the task of the extractor algorithm to the extract signalportions that contain the overlapping multipath symbol copies for asingle symbol.

At operation 208, rising and falling threshold indices are determined bythe multipath symbol extractor. The rising threshold index is determinedat value less than half the peak magnitude value. In some embodiments,the rising threshold index is determined at about 0.35 of the peakmagnitude value. In addition, the start of the first path can beextrapolated by subtracting the result of rising threshold multiplied bythe symbol length from the rising threshold index.

Similarly, the falling threshold index can be determined at 0.1 or lessof the peak magnitude value. Signal transmission paths that are delayedin time typically have much lower magnitudes due to the spreading andabsorption of the acoustic signal. The end of the symbol's final pathcan be determined by first subtracting the falling threshold from 1 andmultiplying the result with the symbol length, and then subtracting theresult of the first step from the falling threshold index. The magnitudesection between the start and stop indices can be further analyzed forthe occurrences of magnitude valleys (i.e., minima). Minima can beidentified by searching for positive slope zero crossing in thedifferentiated magnitude signal. Local minima which extend greater thana chip can indicate that the signal transmission paths are not in fulloverlap and that the signal can be analyzed as two non-overlappingoccurrences.

If no minima are found on either side of the valid peak, the final startand stop indices for the symbol sample window can still be determined.For example, the zero magnitude crossings of the average magnitude atstart and stop positions can be found by linear extrapolation at thethreshold locations using the differentiation magnitude value as slope.If a minimum is found to the left of the peak (in the rising portion ofthe averaged magnitude signal), the start of the symbol sample window isadjusted to exclude the previous non-overlapping symbol occurrence.Since there can be another path interfering with the averaged magnitudesignal, differentiated magnitude signal can no longer be used toestimate the start of the signal, instead the start of window can bedetermined by first multiplying the symbol length with the averagemagnitude and divide by the peak value, and then subtract the result inthe first step from the minimum index. Similarly, the stop index of thewindow can be modified if a minimum is found to the right of the peak.For example, the stop index can be determined by a first step ofdividing the average magnitude by the peak value and subtracting theresult from 1. The result from the first step is multiplied with thesymbol length and then subtracted from the minimum index value.

FIG. 3 illustrates operations of an exemplary method 300 for determiningposition information of a mobile communication device, in accordancewith some embodiments. Other operations in exemplary method 300 can beperformed and the operations can be performed in a different orderand/or vary. As described above, the determination processes can beperformed in a processing unit embedded in mobile communication device106 or performed in a remote processing unit that is communicativelycoupled to mobile communication device 106.

At operation 302, mobile communication device 106 receives and samplesthe acoustic signals transmitted from transmitter station 104. Thereceived acoustic signal 110 is first down-converted to baseband, thensampled at or higher than the chip rate. In some embodiments, thereceived acoustic signal 110 can be oversampled by a factor between 2 to8 to obtain better time and frequency resolution. In some embodiments,the data sampling can be performed at the critical sampling rate (i.e.,the chip sample rate equals to the chip rate).

Mobile communication device 106 can use an energy detection window thathas a length of thirty-two samples to identify a sequence of samplesthat can contain CCK codes. Each CCK code can consist eight complexchips with each complex chip being encoded as one of four possible QPSKsymbols. CCK codes are known from spread-spectrum radio communicationsystems. When used in a coherent radio system, additional two bits ofinformation can be encoded in the quadrature phase of each CCK codechip, enabling eight bits (d₇ . . . d₀) of data to be encoded by eachcode (i.e., 256 different chipping sequences), where do is the leastsignificant bit and the first in time.

At operation 303, Fast Fourier Transform (FFT) is performed on thesampled data, in accordance with some embodiments. An FFT is performedon the 256 samples of the received signal, resulting in 128 bins with afrequency resolution of 7.81 Hz/bin, covering a frequency range of 1kHz. The FFT parameters are selected so as to measure Doppler shift ofup to 1 kHz. This can be sufficient if the signal is transmitted on a 41kHz carrier and if the mobile communication device 106 is carried by aperson in an indoor environment. However, other embodiments can, ofcourse, use different FFT parameters.

At operation 304, Doppler matching templates are created, in accordancewith some embodiments. Doppler matching templates are used tocross-correlate with the Doppler search templates of the receivedacoustic signals to determine the transmitted signal code and a Dopplershift frequency in the received acoustic signal. In some embodiments,the Doppler matching templates can be a signature template stored in amemory device. In some embodiments, other signature templates can beused such as, for example, one or more phase key signals.Cross-correlating received signal with generated Doppler matchingtemplates can remove system response behavior that is not associatedwith the received signal. For example, the cross-correlation process canremove system effects introduced by the driver and transducer in thetransmitter or in the signal transmission pathway due to the microphoneand any subsequent audio filtering. Accounting for such effects in thedetected transmitted signal code prevents degradation of the decodingprocess and therefore the ability to determine the position informationof mobile communication device 106. Such system effects can be accountedfor by characterizing each individual system response function andcompensating the transmitted signal code. In some embodiments, receivedacoustic signals can be measured in an anechoic room and the resultingsignals can be used as Doppler matching templates with each template IDrepresenting a transmitted signal code. In some embodiments whereconventional CCK codes are used, 64 codes can be generated and therefore64 templates are generated. In some embodiments, other numbers oftemplates can be generated depending of the type of transmitted signalcodes used.

The combined system response of transmitter station 104 and circuitry inmobile communication device 106 can be accounted for by measuring aplurality of acoustic signals in a mobile communication device 106 in ananechoic chamber. Transmitter 104 is excited sequentially using allavailable codes in a code sequence. In some embodiments whereconventional CCK codes are used, 64 codes can be generated. The receivedacoustic signal is subsequently sampled at or higher than the chip rate.In some embodiments, the received acoustic signals are sampled at 4times the chip rate. Once a code is identified the 32 complex samplesincluding two buffer samples at either end (36 complex samples), wereextracted and stored as template signals. The accurate location of theCCK code within these 36 samples can be found using a direct correlationdecoding method and stored in the template. In some embodiments, theDoppler search templates can be further processed to ease processing byusing a complex valued FFT routine with zeros being added to the complextime samples. For example 220 zeros can be added to the 36 complexsamples to provide a total of 256 samples. This improves the frequencyresolution achievable for the Doppler shift frequency estimate.Alternatively the complex valued frequency response of the transmitterand receiver signal chains can be characterized individually and storedfor retrieval by the mobile device. These transfer functions cansubsequently be used by the mobile device in the deconvolution step toremove the distortion introduced by these components in the receivedmicrophone signal. Such processing is advantageously done in thefrequency domain.

At operation 306, the magnitude of the received acoustic signal iscross-correlated with the magnitude of the Doppler matching templates inthe frequency domain, in accordance with some embodiments. Each codevalue in the Doppler matching template is evaluated as a candidate forthe transmitted code signal of the received acoustic signal. In someembodiments, a plurality of Doppler search templates are generated usingthe received signal and cross-correlated with the Doppler matchingtemplate to determine a Doppler shift for each Doppler search template.For example, after FFT is performed on the 256 samples of the receivedsignal, 128 bins are generated with a frequency resolution of 7.81Hz/bin, covering a frequency range of 1 kHz. Each bin of the receivedsignal can be a Doppler search template. As described above, FFT hasbeen performed on the received acoustic signal prior to thecross-correlation process and the FFT parameters are selected so as tomeasure Doppler shift frequency of up to about 1 kHz. In someembodiments, the FFT parameters can be selected for the measurement of ahigher or lower Doppler shift frequency. Therefore, thecross-correlation process is performed only over the indices that arewithin a pre-defined maximum Doppler velocity.

At operation 308, the Doppler shift frequency in the received acousticsignal is determined, in accordance with some embodiments. The aim ofthe cross-correlation process is to determine Doppler shift frequencyfor each Doppler search template using one of the 64 CCK codes, wherethe estimated occurrence of Doppler shift is the frequency shift thatproduces the largest cross-correlation peak. Each Doppler matchingtemplate is cross-correlated with a Doppler search template of thereceived acoustic signal and the cross-correlation process is repeatedfor each of the 64 templates until the maximum correlation peak isidentified. The identified Doppler shift frequency template is then usedto identify the transmitted CCK code and to estimate the Doppler-shiftvalue. The largest correlation peak may have to exceed a threshold, orsatisfy some other plausibility criteria, before processing continues.The Doppler shift can be determined with a resolution smaller than thefrequency bin size using peak shape analysis. For example, the peak canbe fitted to a parabolic curve to determine a maximum of the parabola.Certain criteria can be used to determine whether incoming data containsone or more CCK codes rather than high bandwidth noise impulses. In someembodiments, cross correlation value normalized by the peak value of themagnitude of the IQ signal divided by the symbol length is between 0.6and 1.5. In some embodiments, the peak width of the frequency magnitudecorrelation can be of the order of one-tenth of the chip rate.Sub-sampling can provide the benefit of reducing power consumption,especially for mobile device.

After a Doppler shift frequency is determined for each Doppler searchtemplate in the received acoustic signal, the processing unit can shiftall samples of the received acoustic signal by the determined Dopplershift frequency in the direction indicated by the sign of the Dopplershift frequency. As a result, the Doppler shift frequency in thereceived acoustic signals has been compensated and the shifting processgenerates compensated acoustic signals. In some embodiments, the Dopplershift can be applied to the template rather than the measured signal.

At operation 310, a deconvolution decoding process is performed on eachDoppler search template of the compensated acoustic signals, inaccordance with some embodiments. Deconvolution decoding process is analgorithm-based process used to reverse the effects of convolution onrecorded data. The objective of the deconvolution decoding processherein is to determine the solution of a convolution equation of theform f*g=h, where h is the compensated acoustic signals, g is thetransmitted signal code, and f is a response function resulting from adirect signal transmission path and reflected signal transmission paths.The response function f can be represented by a series of complex valuedDirac pulses at various delays, phases and magnitudes. As shown above,the response function f can be determined by the complex division ofF=H/G, with the capitalization signifying the respective Fouriertransformed representations of f, g, h. However, for some transmittedsignal code values, nodes exist at the chirp frequency that can resultin a division by zero. This issue can be solved by comparing themagnitude of the transmitted signal code value with a predeterminedthreshold value, such as, for example, 0.5. If the magnitude of thetransmitted signal code value exceeds the threshold value then theoperation proceeds to perform the complex division to determine theresponse function. If the magnitude is below the threshold value thenthe operation proceeds to set the transmitted signal code value to zero.

As described above, the transmitted signal code g can be padded withzeros up to a length of 256 samples. As a result, the length ofcompensated acoustic signal h also has a length of 256 samples.Typically, peaks are resolved in a duration corresponding to theoriginal length of the response function f. This length is variable andis restricted to be identical to the length of compensated acousticsignal h with one extension. Response function f is circularlycontinuous over its length, i.e., the end samples can be added to thestart to be able to resolve paths which occur close to the origin. Insome embodiments, this was done for the final four samples.

At operation 312, valid Dirac peaks are identified in the responsefunction, in accordance with some embodiments. One of the largestchallenges in the implementation of a deconvolution decoder is toidentify valid Dirac peaks from other peaks that occur due to noise inthe response signal f as well as known processing imperfections in FFTbased operations. The determination of valid Dirac peaks in responsefunctions can be further explained with reference to FIGS. 4 and 5below.

FIG. 4 is a schematic representation of the response function fordeconvolution with the transmitted signal code, in accordance with someembodiments. The response function illustrated in FIG. 4 includes twovalid path peaks: first valid peak 402 and second valid peak 404.Subpeaks are peaks illustrated in FIG. 4 that are not the two valid pathpeaks described above. Subpeaks have magnitudes that can besubstantially similar to the second valid peak 404. Therefore, a methodis needed to extract valid paths from the response function f.

FIG. 5 is a flow chart illustrating operations of an exemplary method500 for extracting valid paths from a response function, in accordancewith some embodiments. Other operations in exemplary method 500 can beperformed and the operations can be performed in a different orderand/or vary.

At operation 502, magnitude of the complex valued response function iscalculated, and the magnitude data is scaled with a power law functionthat compensates partly for the effect of spatial spreading. The indexand magnitude data is then arranged in a descending power scalemagnitude.

At operation 504, a statistical distribution analysis is performed onthe scaled magnitude data to identify outlier peaks based on covariance.The analysis is performed in iterations until the scaled covariancedifference is below a threshold value or all the samples are analyzed.For the remaining set of scaled magnitude data, the covariance iscalculated and stored in a shift register. The difference of the newcovariance between the covariance from the previous iteration iscalculated and scaled the result with the covariance of the originaldata set, and the largest scaled magnitude in the remaining data set isthen removed.

The identified outlier peaks can then be clustered to theircorresponding valid peaks. The clustering process is performed based onthe index of the outlier peaks, and if the outlier peak is separatedfrom the Dirac peak by only one index position they can belong to thecorresponding Dirac peak. In some embodiments, further magnitudecalculations can be done on the original magnitude data without applyingthe power law function. In some embodiments, it is advantageous toperform the above analysis using the RMS operation rather thancovariance operation. The RMS operation is sensitive to a DC offsetshift, which arises when deconvolution is performed using non-matchingcode templates.

At operation 506, peak approximation is performed using sinc envelopereconstruction. Peak magnitude values depend on the distribution ofsamples in relation to the path position. To correct this samplingissue, the path peaks are reconstructed using the sinc function as anapproximation of the Dirac pulse. The corrected magnitudes and locationsare then used in further calculations. The qualified peak magnitude canbe calculated using the root mean square (RMS) noise over the wholesample set minus the peak samples. A local RMS noise can also bedetermined by calculating the RMS noise over a subset of samplessurrounding the peak noise and minus the peak samples. In someembodiments, the subset of samples can be samples included in a windowwith sample length equal to the code length. The maximum signal to noiseratio can then be determined by dividing the largest envelope correctedpeak magnitude by the RMS noise of the whole sample set. The peak signalto noise ratio is determined by dividing the peak magnitude by the localRMS noise, and for each peak the phase of the peak is also determined.Each Doppler search template is cycled through the code templates of theDoppler matching templates, and the combination that generates thehighest signal to noise ratio is determined to be the winning code thatwas actually transmitted by the transmitter. Subpeaks that exceed athreshold value of peak magnitude over local noise can also qualify forthe winning code.

In some embodiments, the processing unit can determine Doppler shiftfrequencies for all signal transmission paths. The Doppler shiftfrequency determined in the description above represents the averageDoppler value of all signal transmission paths. Typically one signaltransmission path dominates the magnitude spectrum and therefore alsodominates the average Doppler shift frequency. This Doppler value can bea good estimate for the Doppler velocity of the signal transmission pathwith the highest peak magnitude. To find the Doppler velocities of otherpaths, the processing unit arranges multiple signal transmission pathsby descending peak magnitude. Doppler corrected templates can begenerated and a delay is applied to the corresponding identified Diracpeak location. An inverse Fast Fourier Transform (IFFT) can be appliedto the Doppler corrected templates for each signal transmission path andsubtracted from the received signal. A Fast Fourier Transform (FFT) isperformed on the remaining signal and the result is cross-correlatedwith the Doppler shift templates to determine Doppler shift frequencyfor each signal transmission path. The determined Doppler velocity canbe stored for the next iteration.

In some embodiments, the processing unit can determine Doppler shiftfrequencies for multiple signal transmission paths simultaneously usinga non-linear optimization routine. The non-linear optimization routinecan be used in complex acoustic reflection cases. Each signaltransmission path between the transmitter station and the mobilecommunication device can be accurately modelled using various parametersand the received acoustic signal can be fitted with the modelling datausing a non-linear curve fit routine. The modelling parameters caninclude a time delay as determined during the peak analysis of thedeconvolution signal. The modelling parameter can also include amagnitude derived from the peak analysis of the deconvolution signal orresolved as a parameter in the non-linear optimization. The modellingparameter can further include an observable arising from the remainingwavelength fraction of the path distance between the transmitter unitand the mobile communication device. The modelling parameter can furtherinclude a frequency index shift arising from relative motion between thetransmitter unit and the mobile communication device. The measured datais then fitted with the modelled data using a non-linear curve fitroutine. In some embodiments, the non-linear curve fit routine canimplement the Levenberg Maquadt method.

The curve fitting procedure can include independent variable series X,dependent variable series Y, and a model objective function thatcalculates the modelled Y values. The modelled Y values are set to areal valued array with a length equal to the incoming IQ data length andwith all values set to zero. The model objective function is configuredto calculate the magnitude difference between measured and model IQ dataat each sample index. The measured IQ data is then converted to a realarray by interleaving the IQ data, resulting in an X array that is twicethe length of the IQ signal length. In the objective function the Xarray is unpacked and re-arranged as complex valued data. Detailedoperations of the non-linear fitting method can be further explainedbelow with reference to FIG. 6 .

FIG. 6 is a flow chart of an exemplary method 600 of determining Dopplervelocities for all signal transmission paths using a non-linear fittingmodel, in accordance with some embodiments. Other operations inexemplary method 600 can be performed and the operations can beperformed in a different order and/or vary.

At operation 602, peaks identified from the deconvolution signal arefiltered. The filtering process can be performed based on thresholdvalues for signal to noise ratios and magnitudes. For example, signalmagnitudes are required to be greater than a certain fraction of the RMSamplitude of the IQ signal, and only peaks meeting or exceeding thesecriteria are used in the subsequent steps.

At operation 604, path template and initial parameter estimation aredetermined for all qualifying paths. The path template is determined forthe ID identified that is shifted by the delay found from thedeconvolution peak location. After the time delay is applied, an inverseFFT can be applied to obtain the time shifted template in the timedomain, ready for use in the model fit. Initial parameter estimationsare also determined for qualifying paths. The initial parameterestimations can include magnitude information determined from thedeconvolution peak value and reconstructed using a sinc envelopefunction. In some embodiments, the magnitude information can also be afixed parameter. The initial parameter estimations can also includephase determined from the deconvolution peak value. The initialparameter estimations can further include an average Doppler frequencyindex shift determined from the frequency correlation with an addedrandom value to seed the fit routine.

At operation 606, IQ model values are determined by adding all modelledpaths. The Doppler induced phase velocity for a path shift is applied toeach path in a similar way as the time delay. In some embodiments, thecomplex IQ values of the path template can be multiplied by a factorthat is determined by the number of samples in the template and thelength of the FFT. The non-linear fit routine can be set to terminateeither when a maximum number of function call is exceeded or whenchanges to the fitting parameters have converged and are less than aspecified tolerance. In some embodiments, the tolerance threshold can be1×10⁻⁵.

At operation 608, the non-linear fit result is evaluated by calculatinga residue value that is the magnitude of the difference between measuredand modelled IQ data at each sample index. In some embodiments, allfitting parameters are evaluated for errors using standard deviations.

At operation 610, qualified paths are determined based on thresholdvalues or suitable criteria. Only qualifying paths are returned as validdecoder results. The suitable criteria can include that path magnitudedivided by the mean residue is required to exceed a threshold fraction.In some embodiments, the threshold fraction can be about 0.75. Thesuitable criteria can also include that the standard deviation of thepath phase is required to be less than a threshold value. In someembodiments, the threshold value can be about 2 radians. The suitablecriteria can further include that the Doppler index shift of the path isless than a threshold value such as, for example, 20 indices. Thesuitable criteria can further include that the Doppler index standarddeviation is less than a threshold value such as, for example, 3.

In some embodiments, an alternative non-linear fit method for acousticmodelling can be implemented. The alternative non-linear fit methodimplements a complete and accurate acoustic model as the objectivefunction to fit the IQ measured data. The fitting parameters can include(i) the position of the mobile communication device; (i) velocity vectorof the mobile communication device; (iii) random phases for each pathused in the acoustic model; (iv) suitable parameters specified toaccount for blockage of the mobile communication device; and (v) anyother suitable parameters. In particular, the parameters used to accountfor blockage of the mobile communication device can include (i)parameters that model the loss of visibility of a reflective structurethat causes complete or partial loss of all associated paths; and (ii)parameters that model attenuation caused by the mobile communicationdevice being enclosed in objects such as bags or pockets.

As described above, the mobile receiver unit can identify a sequence ofsamples that can contain CCK codes. However, for acoustic signals suchas ultrasound signals, the time of flight is comparable to the length ofa time slot of the signal, while the time of flight for a radiofrequency signal is almost negligible. Therefore, the nature ofultrasound signals makes it more challenging to establish the receivedsignal's time of arrival and identify the transmitted codes, especiallywithin an indoor environment with limited space such as hospitals oroffices and in places where multiple transmitters exist. A code keyscheme and method of identifying the code keys described by the presentdisclosure provide the benefit of improving code identify accuracy andreduce latency. Ultrasound code key (UCK) and methods of identifying UCKin the received signals utilize the timing information of the receivedsignal in conjunction with the synchronized and time slotted nature toprovide improved fault tolerance. The identification process alsoprovides information related to ultrasound identification (USID) andlocations of the receiver device. UCK decoders can work under extrememultipath conditions and location of the receiver device can bedetermined when multiple observations of the same code are observed withsimilar received signal strength indication.

The signal's time of arrival and time slot information can be used toimprove USID code resolution in the UCK decoding process. The time slotinformation can include UCK time slot assignment which can provide USIDinformation in the signal rather than sending the USID as a separatesignal. In some embodiments, the USID can be encoded in the ultrasoundsignal channel and is used to establish synchronization with thetransmitter schedule. Time slot information can also provide informationof the receiver device's location. For example, the time slotinformation can be a reliable indicator of the receiver device locationas long as the receiver device is synchronized to the transmitterschedule. In addition, the timing of the signal within the time slot canbe used to determine the code sequence that is most likely beingtransmitted. In some embodiments, each UCK code value transmitted in anarea level location of deployment has an associated time slot. Forexample, a 1 second period can include 16 time slots where each timeslot can be 60 ms in duration.

The incoming acoustic signals, such as ultrasound signals, can becorrelated using a magnitude block window, in accordance with someembodiments of the present disclosure. The window length can correspondto the code length. For example, the block window length can be a lengthequivalent to 8 times 4 IQ samples. The summation of the received signalmagnitudes can be calculated over a certain window width, and the blockwindow can be shifted one sample signal at a time. Convolution of arectangular window is performed over all of the incoming signals. Insome embodiments, the sum of the magnitude for all received 32 samplescan be calculated. In addition, whether the transmitted a signalcontains a signal of the correct length can be determined independentlyof the content of the code.

The correlation can be used to identify and analyze signal peaks. Insome embodiments, signal from a single transmitted path can appear as atriangular-shaped peak. A finite impulse response (FIR) differentiatorcan be applied to the received signal with a length that is a portion ofthe entire code length. The length can be selected between a window ofthe entire code length or a minimum code length that is sufficient toavoid noise sensitivity. In some embodiments, the code length can be 75%of the entire code length (e.g., 24 IQ samples).

The start and end of the signal window can be determined after a signalpeak has been identified. The correlation shape descends on either sideof the peak, thus the start and end of the signal window can beidentified using a threshold value. For example, signal strength greaterthan a magnitude threshold relative to the peak magnitude can beidentified as a portion of the signal. In some embodiments, themagnitude threshold can be between 2% to 20% of the peak magnitude. Insome embodiments, noise can be tolerated in order to obtain a completecopy of the signal path information. The signal window can containmajority of paths that the transmitter signal has followed to reach thereceiver device. The timing of the signal can be stored and used toassess the fitness to a time slot. In some embodiments, the timing ofthe signal can be an estimate time of arrival of the first path. In someembodiments, the peak magnitude is stored as received signal strengthindication.

The signal contained within the signal window can be deconvoluted withall codes relevant in the area. In some embodiments, signals of pathtraces that exceed the signal to noise ratio threshold and frequencycorrelation thresholds can be considered possible valid candidate codes.Doppler offset can be determined for each code to provide the optimumfrequency correlation.

The signal to noise ratios for all the candidate code can be used todetermine probabilities that the code is the correct code transmitted.The “code-is-correct probability” P_(snr) can be determined by applyingerror probability functions to the signal to noise ratios of the allcandidate signal codes. In some embodiments, the signal to noise ratiofor all the candidate codes are measured, and the best signal to noiseratio obtained is set to 0 db. Signal to noise ratio of each candidatecode is then each compared to the best signal to noise ratio tocalculate the probability of the specific code being correct.

The selection of the winning code amongst the candidate codes canadvantageously be based on multiple parameters derived during thedecoding process rather than just the signal-to-noise ratio parameters.The reason for this is that in severe multi-path conditions combinedwith a large possible Doppler range, signal-to-noise contrast betweenthe candidate codes is limited. In these scenarios, other parameters maybe of assistance in a determination of the likelihood of correctness.Such parameters may include signal-to-noise ratios, the Dopplerfrequency shift, magnitude of the peak in the frequency magnitudecorrelation, full width at half-maximum of the peak in the frequencymagnitude correlation, the number of peaks, magnitude of the peak foundin the deconvolution path trace, full width half-maximum of the peakfound in the deconvolution path trace, the number of IQ samplesselected, and the width and magnitude of the correlation window. In anembodiment where a non-linear fit is performed on the IQ data, theparameters may also include the individual Doppler and phase parametersfor each peak identified in the path trace. In some embodiments, arelationship between one or more of these parameters may be a prioridetermined to be relevant in certain scenarios to improve thedetermination of the likelihood of correctness. In such embodiments,these one or more parameters are used in the determination of likelihoodof correctness, much like the approach described above for thesignal-to-noise parameter. In other embodiments, it may not be readilyapparent a priori which parameters may be useful to improve thedetermination of the likelihood of correctness. In certain embodiments,a machine learning approach may be used to improve the accuracy of theapproach, and thereby improve the determination of the likelihood ofcorrectness. As used herein, machine learning seeks to solve theclassification problem, namely to take a set of parameters, where one ormore parameters of the one or more set of parameters are used toclassify a candidate code as either correct or incorrect. Relevantmachine learning methods include the traditional classification methods(such as for example maximum likelihood), as well as support vectormachine, artificial neural networks and the random forest approaches.Thus, in an embodiment, a method to estimate the relationship betweenthese parameters and the likelihood of correctness for a candidate codemay be implemented in the form of a machine learning algorithm. In anembodiment, the machine learning algorithm may be trained using largenumber of parameter sets with a-priori known code winners, with thealgorithm performing a classification of the code winner amongst thecandidate codes. Such parameter data sets may be obtained by recordinglife data in a wide variety of acoustic environments and positions andor orientations of the transmitting and receiving device. Such parametersets may also be generated using a comprehensive acoustic simulation,that uses as a variety of acoustic environments and positions and ororientations of the transmitting and receiving device as input.

The time of arrival information obtained from the correlation peakstarts can be used to determine the signals' time of transmit andcalculate the probability of the signals' timing being correct. Thecandidate codes with high signal to noise ratio are selected and theUSID for these candidate codes are used to search the time slots. Insome embodiments, the time offset for the time slot can be subtractedfrom the time of arrival of the first transmission path to effectivelyestimate the time of arrival for slot 0. Slot 0 can be an initial timeslot among the time slots, and the timing information such as its timeslot width and count can be obtained from the server. When more than onetransmitter unit is involved in transmitting signals, the signal tonoise ratio of the received signals can be used as a weighing factor inthe statistical timing estimate to increase the synchronizationaccuracy. For example, transmitter units closer in distance to thereceiver device can be given more weight while transmitter units furtherin distance are given less weight in the analysis. The weighing analysisremoves unwanted signals received from remote units that are effectivelyfalling out of the time slots. The slot 0 time of arrival can be used todetermine the time fraction of the time of transmit by removing theincrement timing period. For example, the slot 0 time of arrival can bedivided using an estimate of the period count multiplied by the system'srepetition period using a transmit counter. The time of transmit can bedetermined by the fraction in terms of the local clock and removing thesystem's increment period. For example, for a system repetition periodof 1 s and signals received at 1.25 s, 2.25 s, 3.25 s, etc., at thereceiver device, this process can eliminate the increment of period anddetermine the fraction of time is 0.25 s. The time of transmit in termsof local clock is added to the time of transmit history. In someembodiments, the most recent 15-90 s of time of transmit observationsare stored and weighed by signal to noise ratios. The history of time oftransmits can be statistically analyzed to remove outlier peaks. Thehistory of time of transmits can also be used to determine the averageand standard deviation of the time of transmits by using signal to noiseratio to weigh the time of transmit observations.

The time of transmit in terms of local clock can also be used tocalculate probabilities of the time being correct (P_(tim)) for allcandidate codes. For example, the probabilities P_(tim) can becalculated using their respective offset, the time of transmit averageand standard deviation, and a timing error probability function.

The probability for the transmitted code being correct can be calculatedfor all candidate codes. The total probability can be calculated bymultiplying P_(snr) with P_(tim). The winning code to be reported as thedetected code for the signal window can be selected with the highesttotal probability. In some embodiments, codes with total probabilitygreater than 0.5 can be considered valid. It should be noted that thesignal to noise probabilities are used rather than the time of arrivalto weigh the time of transmit observations.

It is to be appreciated that the Detailed Description section, and notthe Abstract of the Disclosure, is intended to be used to interpret theclaims. The Abstract of the Disclosure section may set forth one or morebut not all exemplary embodiments contemplated and thus, are notintended to be limiting to the subjoined claims.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the disclosure that others can, by applyingknowledge within the skill of the art, readily modify and/or adapt forvarious applications such specific embodiments, without undueexperimentation, without departing from the general concept of thepresent disclosure. Therefore, such adaptations and modifications areintended to be within the meaning and range of equivalents of thedisclosed embodiments, based on the teaching and guidance presentedherein. It is to be understood that the phraseology or terminologyherein is for the purpose of description and not of limitation, suchthat the terminology or phraseology of the present specification is tobe interpreted by the skilled artisan in light of the teachings andguidance.

The breadth and scope of the present invention should not be limited byany of the above-described exemplary embodiments, but should be definedonly in accordance with the following claims and their equivalents.

What is claimed is:
 1. A computer-implemented method, comprising: receiving, by a mobile receiving unit, an acoustic signal from a transmitting device via a plurality of transmission paths, wherein the acoustic signal comprises one of a plurality of code keys; correlating the acoustic signal with a magnitude block window to determine a signal peak; deconvolving the signal peak with each of the plurality of code keys to yield a set of valid code keys from the plurality of code keys, wherein the plurality of code keys comprise Quadrature Phase Shift Keying (QPSK)-encoded Complementary-Code-Keying (CCK) codes; determining a first likelihood of correctness for each valid code key based on at least one parameter associated with the acoustic signal; determining a second likelihood of correctness for each valid code key based on a time of arrival; identifying a code key associated with an identity of the transmitting device based on a joint likelihood of correctness based on the first likelihood of correctness and the second likelihood of correctness; and determining a location of the mobile receiving unit using the identity of the transmitting device.
 2. The computer-implemented method of claim 1, wherein the at least one parameter is at least one of a signal-to-noise ratio or a Doppler frequency shift.
 3. The computer-implemented method of claim 1, wherein the at least one parameter is at least one of a number of peaks or a number of In-phase and Quadrature (IQ) samples selected.
 4. The computer-implemented method of claim 1, wherein the at least one parameter is at least one of a magnitude of the signal peak or a full width at half-maximum of the signal peak.
 5. The computer-implemented method of claim 1, wherein the acoustic signal comprises a plurality of time slots and each valid code key is assigned to a time slot of the plurality of time slots.
 6. The computer-implemented method of claim 1, wherein the first likelihood of correctness is further based on a frequency correlation threshold.
 7. The computer-implemented method of claim 1, wherein a length of the magnitude block window is based on a code length of the plurality of code keys.
 8. The computer-implemented method of claim 1, wherein the time of arrival is adjusted for an offset associated with a time slot of a candidate code.
 9. The computer-implemented method of claim 1, wherein a plurality of times of arrival are analyzed statistically to determine the second likelihood of correctness.
 10. The computer-implemented method of claim 1, wherein determining the first likelihood of correctness for each valid code key based on the at least one parameter associated with the acoustic signal further comprises: determining, using a machine learning algorithm, the first likelihood of correctness for each valid code key based on the at least one parameter associated with the acoustic signal.
 11. A mobile communication device configured to determine an identity of a transmitting device, comprising: a transducer configured to receive an acoustic signal from the transmitting device via a plurality of transmission paths, wherein the acoustic signal comprises one of a plurality of code keys; and a processing unit configured to: correlate the received acoustic signal with a magnitude block window to determine a signal peak, the signal peak having a start and an end; deconvolve the signal peak with each of the plurality of code keys to yield a set of valid code keys from the plurality of code keys, wherein the plurality of code keys comprise Quadrature Phase Shift Keying (QPSK)-encoded Complementary-Code-Keying (CCK) codes; determine a first likelihood of correctness for each valid code key based on at least one parameter associated with the acoustic signal; determine a second likelihood of correctness for each valid code key based on a time of arrival; identify a code key associated with the identity of the transmitting device based on a joint likelihood of correctness based on the first likelihood of correctness and the second likelihood of correctness; and determine a location of the mobile communication device using the identity of the transmitting device.
 12. The mobile communication device of claim 11, wherein the at least one parameter is at least one of a signal-to-noise ratio and a Doppler frequency shift.
 13. The mobile communication device of claim 11, wherein the at least one parameter is at least one of a number of peaks and a number of In-phase and Quadrature (IQ) samples selected.
 14. The mobile communication device of claim 11, wherein the at least one parameter is at least one of a magnitude of the signal peak and a full width at half-maximum of the signal peak.
 15. The mobile communication device of claim 11, wherein the acoustic signal comprises a plurality of time slots and each valid code key is assigned to a time slot of the plurality of time slots.
 16. The mobile communication device of claim 11, wherein the first likelihood of correctness is further based on a frequency correlation threshold.
 17. The mobile communication device of claim 11, wherein a length of the magnitude block window is based on a code length of the plurality of code keys.
 18. The mobile communication device of claim 11, wherein the time of arrival is adjusted for an offset associated with a time slot of a candidate code.
 19. The mobile communication device of claim 11, wherein a plurality of times of arrival are analyzed statistically to determine the second likelihood of correctness.
 20. The mobile communication device of claim 11, wherein the processing unit is further configured to determine the first likelihood of correctness for each valid code key using a machine learning algorithm based on the at least one parameter associated with the acoustic signal. 